R7 model family now training
Frontier intelligence, deployed at inference speed.
A research lab building agentic models, self-improving evaluation loops, and secure inference infrastructure for the next generation of AI-native products.
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/experiments/r7-agentic-runtime
EVAL PASS RATE
94.8%
TOKENS / SEC
18.2K
SAFETY LATENCY
41ms
$ r7.run({ tools: graph, policy: strict, memory: live })
✓ routed 128 eval shards · 0 regressions · deployment gate clear
RESEARCH SYSTEMS
Models that reason, route, and recover in production.
The lab operates on a closed-loop stack: frontier-scale training, live tool environments, adversarial evaluation, and deployment gates that keep autonomy observable.
A
Agentic planning
Long-horizon task graphs with tool-use policies, memory checkpoints, and rollback-aware execution.
32K tool traces / hour
I
Adaptive inference
Mixture routing, speculative decoding, and memory locality tuned for sub-second interactive workloads.
18.2K tok/s sustained
E
Adversarial evals
Continuous red-team suites measure autonomy, refusal quality, jailbreak resistance, and tool integrity.
4.7M evals / release
MODEL FABRIC
A single control plane from pretraining to guarded deployment.
release: r7.4.0
01
Train
Multimodal pretraining with synthetic environment traces.
02
Route
Tool graphs select memory, retrieval, code, and action APIs.
03
Evaluate
Adversarial test suites block regressions before release.
04
Deploy
Runtime policies verify every tool call and response path.
Measured where frontier systems fail.
Tool integrity
99.91%
Recovery score
+37.4
Jailbreak resistance
98.2%
SAFETY PROTOCOL
policy.check(response)
→ tool_call verified
→ private data boundary intact
→ hallucination risk below gate
→ release candidate approved
Build on the lab stack before it becomes obvious.
Partner access is opening for teams shipping AI products that need frontier reasoning, high-trust tools, and realtime inference guarantees.
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